Paying Attention: An Examination of Attention and Empathy as They Relate to Buddhist Philosophy
Bibliographic record
Abstract
The human response to the COVID-19 pandemic has exposed a concerning decline in empathy for each other and the planet. A dualistic conception of mind and body coupled with a capitalist society that requires belief in an inherent self to fuel consumerism both complicate our ability to empathize because these ideas reify our conventional self. This paper argues that an understanding of the Buddhist conception of emptiness as explored in Nagarjuna’s Fundamental Wisdom of the Middle Way (Mūlamadhyamakakārikā) paired with mindful observation of embodied physical experience can allow for an understanding of “self” as a web of interacting processes within the larger web of interacting processes which constitutes the world. This can facilitate a shift in our mode of engagement with the world towards one of empathy because it demonstrates the emptiness of essence of an inherent self and instead situates the conventional “self” as interrelated with the world. Touching on related concepts such as Thich Nhat Hanh’s interbeing, this paper argues that contemplating emptiness while practicing Buddhist mindfulness techniques rooted in bodily sensation can facilitate empathy, which allows for the possibility of not only recovering from the COVID-19 pandemic, but also of rebuilding our global community and thriving as a more empathetic society in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".